Optimizing Cryptocurrency Portfolios: A Comparative Study of Rebalancing Strategies

Nichanan Sakolvieng ()
Additional contact information
Nichanan Sakolvieng: Martin de Tours School of Management and Economics, Assumption University, Thailand. Author-2-Name: Sutta Sornmayura Author-2-Workplace-Name: Martin de Tours School of Management and Economics, Assumption University, Thailand. Author-3-Name: Kaimook Numgaroonaroonroj Author-3-Workplace-Name: Martin de Tours School of Management and Economics, Assumption University, Thailand. Author-4-Name: Author-4-Workplace-Name: Author-5-Name: Author-5-Workplace-Name: Author-6-Name: Author-6-Workplace-Name: Author-7-Name: Author-7-Workplace-Name: Author-8-Name: Author-8-Workplace-Name:

GATR Journals from Global Academy of Training and Research (GATR) Enterprise

Abstract: " Objective - This study aims to contribute to the field of cryptocurrency portfolio management and rebalancing strategies by empirically investigating the impact of different allocation frequencies and threshold percentages on the risk-adjusted returns of cryptocurrency portfolios. Methodology/Technique – Utilizing a simulation of 10,000 cryptocurrency portfolios comprising seven assets, including Ethereum (ETH), Bitcoin (BTC), Tether (USDT), Litecoin (LTC), Solana (SOL), Dogecoin (DOGE), and Polygon (MATIC), this study examines and compares the effects of different allocation frequencies (daily, weekly, and monthly) in time-based rebalancing and various threshold percentages (5%, 10%, and 15%) in threshold-based strategies on the portfolios' risk-adjusted returns, using the Sharpe ratio. The performance of these strategies is also compared with a passive buy-and-hold strategy. Findings – The research reveals statistically significant differences in the risk-adjusted returns between the buy-and-hold strategy and the daily rebalancing and threshold-based strategies with 5% and 10% threshold percentages. The daily rebalancing strategy demonstrates a higher Sharpe ratio, while lower threshold percentages lead to better risk-adjusted returns. Novelty – These empirical findings, using a simulation of 10,000 cryptocurrency portfolios, provide valuable insights into optimizing cryptocurrency portfolio performance through rebalancing strategies. Additionally, they highlight the effectiveness of implementing rebalancing techniques in cryptocurrency portfolios, contributing to the understanding of rebalancing optimization in this domain. Type of Paper - Empirical"

Keywords: Cryptocurrency; Mean-Variance Optimization; Portfolio Management; Rebalancing Strategies; Risk-Adjusted Returns (search for similar items in EconPapers)
JEL-codes: G11 G19 (search for similar items in EconPapers)
Pages: 16
Date: 2024-03-31
New Economics Papers: this item is included in nep-pay, nep-rmg and nep-sea
References: View references in EconPapers View complete reference list from CitEc
Citations:

Published in Journal of Finance and Banking Review, Volume 8, Issue 4

Downloads: (external link)
http://gatrenterprise.com/GATRJournals/JFBR/pdf_fi ... tta%20Sornmayura.pdf (application/pdf)
http://gatrenterprise.com/GATRJournals/online_submission.html

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gtr:gatrjs:jfbr220

DOI: 10.35609/jfbr.2024.8.4(1)

Access Statistics for this paper

More papers in GATR Journals from Global Academy of Training and Research (GATR) Enterprise
Bibliographic data for series maintained by Prof. Dr. Abd Rahim Mohamad ().